SoroushVahidi/scidocs huggingface dataset:
Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks
(2015)

In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members’ similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.
Impact of reducing STI-induced stress on layout dependence of MOSFET characteristics
(2004)

Active-area layout dependence of MOSFET parametric characteristics and its reduction by reducing shallow trench isolation (STI)-induced mechanical stress were investigated. Threshold voltages (V/sub th/) and saturation drain currents (I/sub ds/) become sensitive to the active-area layout of MOSFET in scaled-down technology. This phenomenon is the effect of mechanical stress from STI edge, which reduces impurity diffusion in channel region and enhances carrier mobility. To reduce the STI-induced stress, we examined STI-wall-oxide nitridation and STI gap-fill-oxide densifying in pure N/sub 2/ ambient. These processes reduced the reoxidation of the STI wall oxide, therefore reduced the STI-induced stress. According to the new STI process, the active-area layout dependence of V/sub th/ and I/sub ds/ were reduced successfully.
Kijken naar porno: subcorticale hersenactiviteit weerspiegelt negatieve maar niet positieve seksuele associaties
(2013)

Aan de ene kant kunnen mensen visuele seksuele stimulatie (VSS) ervaren als plezierig, maar aan de andere kant kunnen mensen VSS ook ervaren als iets “viezigs” en negatief. Dit onderzoek richt zich op de vraag hoe het brein bij vrouwen reageert op VSS en hoe de hersenactiviteit samenhangt met hun impliciete en expliciete attitudes ten aanzien van VSS. Vrouwen zonder seksuele klachten (N = 20) bekeken een reeks afbeeldingen uit diverse categorieën (seksuele penetratie, walging, neutraal) in een fMRI-scanner. Met behulp van een reactietijd-taak (Implicit Association Test) werd vervolgens de sterkte van hun impliciete seks-vies versus seks-lekker associaties gemeten. Daarnaast beoordeelden ze alle afbeeldingen op een visuele analoge schaal als meting van hun expliciete attitude. De hersenactiviteit in reactie op afbeeldingen van seksuele penetratie vertoonde grote overeenkomst met die op afbeeldingen van walgelijke plaatjes. Specifiek de hersenactiviteit in reactie op de seksuele penetratieafbeeldingen bleek te variëren als functie van de impliciete attitude ten aanzien van de seksuele penetratie-plaatjes. Hoe sterker de impliciete associatie van seks met walging des te sterker de activiteit in de basale voorhersenen (inclusief de nucleus accumbens en de bed nucleus van de stria terminalis), de middenhersenen, en amygdala in reactie op het zien van de seksuele penetratie plaatjes. De betrokken hersengebieden zijn veelal geassocieerd met visuele seksuele verwerkingsprocessen. De huidige resultaten laten zien dat de activatie van deze gebieden niet zondermeer een positieve appreciatie van seksuele stimuli impliceert, maar ook kan duiden op een negatieve of ambivalente houding ten aanzien van seksuele stimuli.
Automatic detection of motion artifacts in MR images using CNNS
(2017)

Considerable practical interest exists in being able to automatically determine whether a recorded magnetic resonance image is affected by motion artifacts caused by patient movements during scanning. Existing approaches usually rely on the use of navigators or external sensors to detect and track patient motion during image acquisition. In this work, we present an algorithm based on convolutional neural networks that enables fully automated detection of motion artifacts in MR scans without special hardware requirements. The approach is data driven and uses the magnitude of MR images in the spatial domain as input. We evaluate the performance of our algorithm on both synthetic and real data and observe adequate performance in terms of accuracy and generalization to different types of data. Our proposed approach could potentially be used in clinical practice to tag an MR image as motion-free or motion-corrupted immediately after a scan is finished. This process would facilitate the acquisition of high-quality MR images that are often indispensable for accurate medical diagnosis.
Semi-supervised Deep Learning with Memory
(2018)

We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rely on the up-to-date “network-in-training” to formulate the semi-supervised learning objective. This ignores both the discriminative feature representation and the model inference uncertainty revealed by the network in the preceding learning iterations, referred to as the memory of model learning. In this work, we propose a novel Memory-Assisted Deep Neural Network (MA-DNN) capable of exploiting the memory of model learning to enable semi-supervised learning. Specifically, we introduce a memory mechanism into the network training process as an assimilation-accommodation interaction between the network and an external memory module. Experiments demonstrate the advantages of the proposed MA-DNN model over the state-of-the-art semisupervised deep learning methods on three image classification benchmark datasets: SVHN, CIFAR10, and CIFAR100.
A real-time object detecting and tracking system for outdoor night surveillance
(2008)

Autonomous video surveillance and monitoring has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments. However, object detection and tracking at night remain very important problems for visual surveillance. The objects are often distant, small and their signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame will do not work. In this paper, a novel real time object detection algorithm is proposed for night-time visual surveillance. The algorithm is based on contrast analysis. In the first stage, the contrast in local change over time is used to detect potential moving objects. Then motion prediction and spatial nearest neighbor data association are used to suppress false alarms. Experiments on real scenes show that the algorithm is effective for night-time object detection and tracking. 2007 Published by Elsevier Ltd on behalf of Pattern Recognition Society.
Thermal management and cooling of windings in electrical machines for electric vehicle and traction application
(2017)

Conventional electrical machine cooling includes housing fins, shaft-mounted fan, and water jacket. In these cases, heat from the copper loss of windings needs to pass through either the stator core or the air between windings and the housing. Because of the large thermal resistance in the path, these methods are sometimes not efficient enough for high torque density machines. Overheat in windings causes failure in the insulation and damages the machine. Many technologies that facilitate winding cooling have been investigated in the literature, such as winding topologies with more efficient heat dissipation capability, impregnation material with high thermal conductivity, and advanced direct winding. This paper reviews and classifies thermal management and cooling methods to provide a guideline for high torque density electrical machine design for a better winding thermal management.
Multisensor data fusion: Target tracking with a doppler radar and an Electro-Optic camera
(2011)

This paper addresses the problem of multisensor data fusion for target tracking using a Doppler radar with range rate measurements and an Electro-Optic (EO) camera. We present three fusion architectures, named FA1-FA3, to perform data fusion using the above mentioned sensors. FA1 and FA2 are distributed fusion architectures employing the information matrix fusion method with dynamic feedback. In FA1, radar and camera pseudo measurements are formed that allow us to make use of a linear Kalman Filter (KF) for the radar local filter and an Extended Kalman Filter (EKF) for the EO camera local filter. In FA2, the radar and camera measurements are used directly and therefore the system comprises two EKFs. FA3 is a centralised architecture where the data fusion is performed by way of the measurement fusion method. The final contribution of this paper is a performance comparison of these sensor data fusion techniques when making use of range rate measurements. In order to evaluate the performance of the fusion architectures, Monte Carlo simulations are performed and two filter metrics are presented: an absolute metric - the root mean squared error (RMSE) and a performance metric - the average normalised estimation error squared (ANEES). The results show that the fusion architectures presented are accurate, stable and credible.
Two Bitcoins at the Price of One? Double-Spending Attacks on Fast Payments in Bitcoin
(2012)

Bitcoin is a decentralized payment system that is based on Proof-of-Work. Bitcoin is currently gaining popularity as a digital currency; several businesses are starting to accept Bitcoin transactions. An example case of the growing use of Bitcoin was recently reported in the media; here, Bitcoins were used as a form of fast payment in a local fast-food restaurant. In this paper, we analyze the security of using Bitcoin for fast payments, where the time between the exchange of currency and goods is short (i.e., in the order of few seconds). We focus on doublespending attacks on fast payments and demonstrate that these attacks can be mounted at low cost on currently deployed versions of Bitcoin. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast transactions are not always effective in resisting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we leverage on our findings and propose a lightweight countermeasure that enables the detection of doublespending attacks in fast transactions.
Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem
(2014)

We present an empirical investigation into the prevalence and impact of distributed denial-of-service (DDoS) attacks on operators in the Bitcoin economy. To that end, we gather and analyze posts mentioning “DDoS” on the popular Bitcoin forum bitcointalk.org. Starting from around 3 000 different posts made between May 2011 and October 2013, we document 142 unique DDoS attacks on 40 Bitcoin services. We find that 7% of all known operators have been attacked, but that currency exchanges, mining pools, gambling operators, eWallets, and financial services are much more likely to be attacked than other services. Not coincidentally, we find currency exchanges and mining pools are much more likely to have DDoS protection such as CloudFlare, Incapsula, or Amazon Cloud. We show that those services that have been attacked are more than three times as likely to buy anti-DDoS services than operators who have not been attacked. We find that big mining pools (those with historical hashrate shares of at least 5%) are much more likely to be DDoSed than small pools. We investigate Mt. Gox as a case study for DDoS attacks on currency exchanges and find a disproportionate amount of DDoS reports made during the large spike in trading volume and exchange rates in spring 2013. We conclude by outlining future opportunities for researching DDoS attacks on Bitcoin.
Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk
(2013)

Bitcoin has enjoyed wider adoption than any previous crypto-currency; yet its success has also attracted the attention of fraudsters who have taken advantage of operational insecurity and transaction irreversibility. We study the risk investors face from Bitcoin exchanges, which convert between Bitcoins and hard currency. We examine the track record of 40 Bitcoin exchanges established over the past three years, and find that 18 have since closed, with customer account balances often wiped out. Fraudsters are sometimes to blame, but not always. Using a proportional hazards model, we find that an exchange’s transaction volume indicates whether or not it is likely to close. Less popular exchanges are more likely to be shut than popular ones. We also present a logistic regression showing that popular exchanges are more likely to suffer a security breach.
Game-Theoretic Analysis of DDoS Attacks Against Bitcoin Mining Pools
(2014)

One of the unique features of the digital currency Bitcoin is that new cash is introduced by so-called miners carrying out resourceintensive proof-of-work operations. To increase their chances of obtaining freshly minted bitcoins, miners typically join pools to collaborate on the computations. However, intense competition among mining pools has recently manifested in two ways. Miners may invest in additional computing resources to increase the likelihood of winning the next mining race. But, at times, a more sinister tactic is also employed: a mining pool may trigger a costly distributed denial-of-service (DDoS) attack to lower the expected success outlook of a competing mining pool. We explore the trade-off between these strategies with a series of game-theoretical models of competition between two pools of varying sizes. We consider differences in costs of investment and attack, as well as uncertainty over whether a DDoS attack will succeed. By characterizing the game’s equilibria, we can draw a number of conclusions. In particular, we find that pools have a greater incentive to attack large pools than small ones. We also observe that larger mining pools have a greater incentive to attack than smaller ones.
Towards Risk Scoring of Bitcoin Transactions
(2014)

If Bitcoin becomes the prevalent payment system on the Internet, crime fighters will join forces with regulators and enforce blacklisting of transaction prefixes at the parties who offer real products and services in exchange for bitcoin. Blacklisted bitcoins will be hard to spend and therefore less liquid and less valuable. This requires every recipient of Bitcoin payments not only to check all incoming transactions for possible blacklistings, but also to assess the risk of a transaction being blacklisted in the future. We elaborate this scenario, specify a risk model, devise a prediction approach using public knowledge, and present preliminary results using data from selected known thefts. We discuss the implications on markets where bitcoins are traded and critically revisit Bitcoin’s ability to serve as a unit of account.
Mastering Digital Transformation: the Path of a Financial Services Provider towards a Digital Transformation Strategy
(2017)

To master the challenges of a digital transformation and to systematically address IT’s multifaceted transformative impacts on an organization’s inner and outer environments, top management is increasingly formulating and implementing a digital transformation strategy (DTS). To date, there have been few details of DTS formation concerning its underlying processes and activities. In this study, an interpretive case study approach is employed and DTS formation is investigated from a process/activity perspective. By using an activity-based process model that builds on IS strategizing, an in-depth case study at a large financial services provider was conducted. The results show that this DTS was predominantly shaped by a diversity of emergent strategizing activities through a bottom-up process and prior to the introduction of a holistic approach by top management. Top management then sought to formalize emergent strategy contents by formulating and implementing a DTS that comprised a shared target picture, distinct digital transformation governance, and measures to increase the share of deliberate strategy contents. Besides providing practical implications for DTS formulation and implementation, this study contributes to the literature on digital transformation, IS strategy, and IS strategizing.
A comprehensive physical model for light reflection
(1991)

A new general reflectance model for computer graphics is presented. The model is based on physical optics and describes specular, directional diffuse, and uniform diffuse reflection by a surface. The reflected light pattern depends on wavelength, incidence angle, two surface roughness parameters, and surface refractive index. The formulation is self consistent in terms of polarization, surface roughness, masking/shadowing, and energy. The model applies to a wide range of materials and surface finishes and provides a smooth transition from diffuse-like to specular reflection as the wavelength and incidence angle are increased or the surface roughness is decreased. The model is analytic and suitable for Computer Graphics applications. Predicted reflectance distributions compare favorably with experiment. The model is applied to metallic, nonmetallic, and plastic materials, with smooth and rough surfaces.
Labor Costs and Foreign Direct Investment : A Panel VAR Approach
(2017)

This paper examines the endogenous interaction between labor costs and Foreign Direct Investment (FDI) in the OECD countries via the Panel VAR approach under system GMM estimates for the period 1995–2009. The available data allows identifying the relevance of the components of labor costs, and allows a detailed analysis across different sectors. Empirical findings have revealed that sectoral composition of FDI and the decomposition of labor costs play a significant role in investigating the dynamic association between labor costs and FDI. Further, results suggest that labor market policies should focus on productivity-enhancing tools in addition to price hindering tools.
Hairstyle recommendation system for women
(2016)

A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.
Harmonic Resonance Evaluation for Hub Traction Substation Consisting of Multiple High-Speed Railways
(2017)

In many hub traction power-supply systems (TPSSs), the 27.5 kV busbars in the traction substation (TSS) have supplied several railway lines in a local area for reducing the number of TSSs. However, the multiple railway lines connected to the busbar may affect the resonance behaviors of the entire system. It is therefore necessary to investigate the resonance behaviors under such hub TPSS conditions since the harmonic current collection of all railway lines will aggravate the resonance problem. Thus, resonance-mode assessment and its sensitivity method is adopted for investigating the resonance behaviors of hub TPSS affected by different numbers of connected lines, lengths of supply lines, system capacities, etc. The numerical results of modal sensitivity indices are compared to investigate and quantify the harmonic resonance issue caused by multiple electrical railway lines. In addition, the resonance frequency-shift technique and modal impedance sensitivity index-based method are also employed to mitigate the critical harmonic resonances. The results illustrate that it is effective to analyze resonance issues by adopting the harmonic model and modal methods in this paper. The two types of resonance behaviors (i.e., primary resonance and resonance band) caused from the hub TPSS have been fully investigated, and the resulting harmonic distortion of hub TPSS is suppressed.
Communication-Efficient Distributed Statistical Inference
(2016)

We present a Communication-efficient Surrogate Likelihood (CSL) framework for solving distributed statistical inference problems. CSL provides a communication-efficient surrogate to the global likelihood that can be used for low-dimensional estimation, high-dimensional regularized estimation and Bayesian inference. For low-dimensional estimation, CSL provably improves upon naive averaging schemes and facilitates the construction of confidence intervals. For high-dimensional regularized estimation, CSL leads to a minimax-optimal estimator with controlled communication cost. For Bayesian inference, CSL can be used to form a communication-efficient quasi-posterior distribution that converges to the true posterior. This quasi-posterior procedure significantly improves the computational efficiency of MCMC algorithms even in a non-distributed setting. We present both theoretical analysis and experiments to explore the properties of the CSL approximation.
THE AUDIT RISK MODEL UNDER THE RISK OF FRAUD
(2001)

In this article, we derive an audit risk formula for a simple situation. This formula closely resembles the SAS 47 model when we assume that no material misstatement due to fraud exists. A simple case illustrates how the risk of material misstatement due to management fraud impacts audit risk and how performing special audit procedures to detect such irregularities can decrease overall audit risk. While SAS 53 requires auditors to assess the risk of errors and irregularities and plan the audit to provide reasonable assurance of detecting errors and irregularities, the audit risk model of SAS 47 does not directly address the risk of management fraud. This article develops an audit risk model using the belief-function framework that considers the risks faced by auditors due to random errors, defalcations (employee fraud) and management fraud. We consider two cases. In the first, we consider only affirmative items of evidence and derive an analytical formula for the audit risk. In the second, we consider mixed items of evidence (both affirmative and negative), which models the situation more frequently faced by auditors. We demonstrate that a serious underestimation of audit risk can occur if the audit risk model is used without specifically considering the risks associated with management fraud.